Metastatic Cancer Tracking Gets Computational Upgrade

Tracing and predicting the course that metastatic cancers take through the body may shed light on cellular changes that lead to metastasis. Metastatic disease causes about 90% of cancer deaths from solid tumors—masses of cells that grow in organs such as the breast, prostate, or colon. Understanding the drivers of metastasis could lead to new treatments aimed at blocking the process of cancer spreading through the body. Now, Princeton University investigators have developed a new computational method that increases the ability to track the spread of cancer cells. Findings from the new study were published recently in Nature Genetics, in an article entitled “Inferring Parsimonious Migration Histories for Metastatic Cancers.”

“Are there specific changes, or mutations, within these cells that allow them to migrate?” asked senior study investigator Ben Raphael, Ph.D., a professor of computer science at Princeton. “This has been one of the big mysteries.”

In the current study, Dr. Raphael and his colleagues presented an algorithm that can track cancer metastasis by integrating DNA sequence data with information on where cells are located in the body. They call it MACHINA, which stands for metastatic and clonal history integrative analysis.

“Our algorithm enables researchers to infer the past process of metastasis from DNA sequence data obtained at present,” Dr. Raphael noted. “The datasets we get these days are very complex, but complex data sets don't always require complex explanations.”

The new technique yields a clearer picture of cancer migration histories than previous studies that relied on methods based on DNA sequences alone. Some of these studies inferred complex migration patterns that didn't reflect current knowledge of cancer biology.

The Princeton team found that by simultaneously tracing cells' mutations and movements, MACHINA found that metastatic disease in some patients could result from fewer cellular migrations than previously thought. For example, in one breast cancer patient, a previously published analysis proposed that metastatic disease resulted from 14 separate migration events, while MACHINA suggested that a single secondary tumor in the lung seeded the remaining metastases through just five cell migrations. In addition to a breast cancer dataset, Dr. Raphael and his team applied their algorithm to analyze metastasis patterns from patients with melanoma, ovarian, and prostate cancers.

Several additional features helped improve MACHINA's accuracy. The algorithm includes a model for the comigration of genetically different cells, based on experimental evidence that tumor cells can travel in clusters to new sites in the body. Moreover, it also accounts for the uncertainty in DNA data that comes from sequencing mixtures of genetically distinct tumor cells and healthy cells.

This approach overcomes a number of challenges to draw meaningful conclusions from the “difficult-to-analyze, noisy” data that result from tumor DNA sequencing said Andrea Sottoriva, Ph.D., a fellow in evolution and cancer at The Institute of Cancer Research, London. “I predict this new method will be of widespread use to the genomic community and will shed new light on the deadliest phase of cancer evolution.”

MACHINA's development paves the way for a broader examination of metastasis patterns in large cohorts of cancer patients, which could reveal key mutations that cause different types of cancer to spread. Additionally, Dr. Raphael plans to make the method more powerful by incorporating data from tumor DNA and tumor cells that circulate in the bloodstream, as well as epigenetic changes—reversible chemical modifications of DNA.

“A better algorithm is like a better microscope,” Dr. Raphael concluded. “When you look at nature with a magnifying glass, you may miss important details. If you look with a microscope, you can see much more.”